Chapter 3 Abstract: Ecological niche can drive the genomic diversification of closely related bacterial species. Here, I apply genomic analyses to the genus Psychrobacter, known for its particularly wide ecological distribution. I performed a pan-genome analysis, microbial pan-genome wide association, ancestral character estimations, and analysis of selection on protein sequences of 85 strains of Psychrobacter to clarify the interactions between isolation source and Psychrobacter genome evolution. There is some evidence that Psychrobacter isolation source correlates with available gene pool; each isolation source - from warm-bodied hosts such as mammals or birds, other hosts like fish or invertebrates, to food processing, marine, or terrestrial environments - is correlated with unique genes from different COG categories. Strains isolated from invertebrate and fish hosts, as well as food processing environments, have higher numbers of total genes, though fewer unique genes than strains from mammals, birds, or marine environments. After accounting for population structure, however, very few genes correlate with isolation source; there is some evidence for increased horizontal gene transfer in strains isolated from fish and invertebrates, and evidence for increased biofilm formation in strains isolated from food-processing environments. Ancestral character estimation supports that Psychrobacter are descended from a warm host-associated bacterium. Finally, there is no correlation between isolation source and cold-adaptation of proteins. Overall, my results show that isolation source has some impact on Psychrobacter genome evolution, though the population structure of the genus makes it difficult to disentangle its effects.

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R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

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attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

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 [5] micropan_2.1      igraph_1.2.5      microseq_2.1.2    rlang_0.4.7      
 [9] data.table_1.13.0 tibble_3.0.3      phytools_0.7-47   maps_3.3.0       
[13] ape_5.4-1         reshape2_1.4.4    seqinr_3.6-1      ggsignif_0.6.0   
[17] ggpubr_0.4.0      tidyr_1.1.2       dplyr_1.0.2       ggplot2_3.3.2    
[21] stringr_1.4.0    

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  [7] htmlTable_2.0.1         base64enc_0.1-3         rstudioapi_0.11        
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 [19] compiler_3.6.3          backports_1.1.9         Matrix_1.2-18          
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 [25] tools_3.6.3             coda_0.19-3             gtable_0.3.0           
 [28] glue_1.4.2              clusterGeneration_1.3.4 gmodels_2.18.1         
 [31] fastmatch_1.1-0         Rcpp_1.0.5              carData_3.0-4          
 [34] cellranger_1.1.0        raster_3.3-13           vctrs_0.3.4            
 [37] spdep_1.1-5             gdata_2.18.0            nlme_3.1-149           
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 [43] lifecycle_0.2.0         phangorn_2.5.5          gtools_3.8.2           
 [46] rstatix_0.6.0           LearnBayes_2.15.1       MASS_7.3-52            
 [49] scales_1.1.1            hms_0.5.3               promises_1.1.1         
 [52] parallel_3.6.3          expm_0.999-5            RColorBrewer_1.1-2     
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 [58] rpart_4.1-15            latticeExtra_0.6-29     stringi_1.4.6          
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 [91] sp_1.4-2                crayon_1.3.4            car_3.0-9              
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[100] forcats_0.5.0           digest_0.6.25           classInt_0.4-3         
[103] xtable_1.8-4            httpuv_1.5.4            numDeriv_2016.8-1.1    
[106] munsell_0.5.0           beeswarm_0.2.3          quadprog_1.5-8         
sessionInfo()

Pan-genome summary

genematrix = psych_gpa[,10:ncol(psych_gpa)] #or whichever column your isolate data starts at
rownames(genematrix) = psych_gpa$geneCluster
genematrix = apply(genematrix,  2,  function(x) gsub("^$|^ $",  NA,  x)) #replace empty cells with NA
transform_func = function(x) { if (is.na(x) > 0) { r = 0 } else { r = 1 } } #creates a function to recognize when a cell has information in it (in this case,  when an isolate HAS a gene) and replace that cell's contents with "1". otherwise,  replace that cell's contents with "0"
genematrix_binary <- apply(genematrix, 2, function(x){sapply(x,  transform_func)}) #loop the function created in the step above through our transposed gene matrix
genematrix_binary_t = t(genematrix_binary) #transpose the matrix; need strains to be on the rows and genes to be on the columns
genematrix_binary_t = as.data.frame(genematrix_binary_t)
genematrix_binary_t = genematrix_binary_t %>%
  mutate_if(is.factor, as.numeric) #t() will transform your data into the "matrix" format, and treewas wants it in a dataframe format, so this transforms it back
rownames(genematrix_binary_t) = colnames(genematrix_binary)
#to produce output file "genematrix_for_treewas_fixeddashes.txt"
genes_mat = genematrix 
genes_mat_numbered = genes_mat
for(i in 1:nrow(genes_mat)) {
  for (j in 1:ncol(genes_mat)) {
    cell = genes_mat[i,j]
    num = str_count(cell, ",") + 1
    if (is.na(cell) == TRUE) {num = 0}
    genes_mat_numbered[i,j] = num
  }
}
rownames(genes_mat_numbered) = psych_gpa$geneCluster
genes_mat_numbered = as.data.frame(genes_mat_numbered)
genes_mat_numbered = genes_mat_numbered %>%
  mutate_all(as.character) %>%
  mutate_all(as.numeric)
library(micropan)
genes_mat_forpan = t(genes_mat_numbered)
colnames(genes_mat_forpan) = rownames(genes_mat)
rare = rarefaction(pan.matrix = genes_mat_forpan, n.perm = 999)
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New names:
* `` -> ...2
* `` -> ...3
* `` -> ...4
* `` -> ...5
* `` -> ...6
* ...
rare.melt = reshape2::melt(rare, id = c("Genome"))
(panrarefaction = ggplot(data = rare.melt, aes(x = Genome, y = value)) + 
    geom_boxplot(aes(x = Genome, group = Genome), outlier.shape = NA) + 
    theme_classic() + 
    xlab("Number of Genomes") + ylab("Number of Novel Orthologs"))

heaps(pan.matrix = genes_mat_forpan, n.perm = 999)
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  Intercept       alpha 
579.6761870   0.4551001 
warm = psych_strain_collection %>% filter(ISME_source == "warm host") %>% select(strainID) %>% unlist() %>% unname()
other = psych_strain_collection %>% filter(ISME_source == "other host") %>% select(strainID) %>% unlist() %>% unname()
food = psych_strain_collection %>% filter(ISME_source == "food") %>% select(strainID) %>% unlist() %>% unname()
marine = psych_strain_collection %>% filter(ISME_source == "marine") %>% select(strainID) %>% unlist() %>% unname()
terrestrial = psych_strain_collection %>% filter(ISME_source == "terrestrial") %>% select(strainID) %>% unlist() %>% unname()
allseqsum = rowSums(genes_mat_numbered)
names(allseqsum) = rownames(genes_mat)
allisosum = rowSums(genematrix_binary)
names(allisosum) = rownames(genes_mat)
pangenome_by_isolation_summary = rbind(allseqsum, allisosum)
pangenome_by_isolation_summary = t(pangenome_by_isolation_summary)
pangenome_by_isolation_summary = as.data.frame(pangenome_by_isolation_summary)
pangenome_by_isolation_summary$geneCluster = rownames(pangenome_by_isolation_summary)
pangenome_by_isolation_summary$overall_pan_cat = NA
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum >= 84] <- "core"
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum <= 83 & pangenome_by_isolation_summary$allisosum >= 77] <- "soft core"
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum <= 76 & pangenome_by_isolation_summary$allisosum >= 2 ] <- "shell"
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum == 1 ] <- "cloud"
pangenome_by_isolation_summary$overall_pan_cat = factor(pangenome_by_isolation_summary$overall_pan_cat, 
                                                        levels = c("core", "soft core", "shell", "cloud"))

Figure 3.1 pan-genome rarefaction and categories

(pancategory_count = ggplot(data = pangenome_by_isolation_summary, aes(x = overall_pan_cat)) + 
    geom_bar(stat = 'count') +
    theme_classic()
)

ggarrange(pancategory_count, panrarefaction, align = 'h', widths = c(1, 2))

Figure 3.2 functional analysis of different pan-genome categories

COG_multi_cats = multicog_geneclusters %>% filter(COG2 != "")
#just realized that this does not have every COG cat for every strain, for example there is no COG CAT "B" for frigidicola_056...
#will have to make a new data frame to populate from scratch
COG_multi_cats_separated = data.frame()
for(i in 1:nrow(COG_multi_cats)) {
  gen = COG_multi_cats[i,1]
  cat = COG_multi_cats[i,2]
  COG1 = COG_multi_cats[i,3]
  dummy1 = c(gen, cat, COG1)
  COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy1), stringsAsFactors = F)
  COG2 = COG_multi_cats[i,4]
  dummy2 = c(gen, cat, COG2)
  COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy2), stringsAsFactors = F)
  COG3 = COG_multi_cats[i,5]
  if (COG3 != ""){
    dummy3 = c(gen, cat, COG3)
    COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy3), stringsAsFactors = F)
  }
  COG4 = COG_multi_cats[i,6]
  if (COG4 != ""){
    dummy4 = c(gen, cat, COG4)
    COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy4), stringsAsFactors = F)
  }
}
colnames(COG_multi_cats_separated) = c("geneCluster", "pan_category", "COG_category")
COG_single_cats = multicog_geneclusters %>% filter(COG2 == "") %>%
  select(geneCluster, pan_category, COG1)
colnames(COG_single_cats)[3] = "COG_category"
multiCOG_genes_corrected = rbind(COG_multi_cats_separated, COG_single_cats)
multiCOG_genes_corrected$pan_category = factor(multiCOG_genes_corrected$pan_category, 
                                               levels = c("core", "softcore", "shell", "cloud"))
multiCOG_genes_corrected$COG_category = 
  factor(multiCOG_genes_corrected$COG_category, levels = c("D", "M", "N", "O", "T", "U", "V", "W", "Z",
                                                "A", "B","J", "K", "L","C", "E", "F", "G", "H", "I", "P", "Q",
                                                "S", "X"))
COGcols = c("#72300C", "#93074D", "#D60D69", "#ED5FA6", "#CE1B13", "#A50505", "#E5530A", "#E2770C", "#F9A138", 
         "#F7F072", "#F7D239", "#CCF43B", "#5BB73D", "#057F28", 
         "#05541A", "#20998B", "#60B1F4", "#1458F2", "#5141AF", "#1F048E", "#310872", "#520775", 
         "#9F9FA0", "#39383A")
(function_pancat = ggplot(data = multiCOG_genes_corrected, 
                          aes(x = pan_category, fill = COG_category)) + 
    geom_bar(stat = 'count', position = 'fill') + 
    scale_fill_manual(values = COGcols) +
    theme_classic())

COG_pancat_chi = chisq.test(t(table(multiCOG_genes_corrected$pan_category, multiCOG_genes_corrected$COG_category)))
Chi-squared approximation may be incorrect
COG_pancat_chi

    Pearson's Chi-squared test

data:  t(table(multiCOG_genes_corrected$pan_category, multiCOG_genes_corrected$COG_category))
X-squared = 2748, df = 69, p-value < 2.2e-16
COG_pancat_cor = corrplot(COG_pancat_chi$residuals, is.cor = FALSE)

#add function_pancat and COG_pancat_cor together in Illustrator

Figure 3.3 and stats comparisons - proportion of genome devoted to each pan genome category

psych_PCAT = left_join(psych_PCAT, pangenome_by_isolation_summary[,3:4])
Joining, by = "geneCluster"
test = left_join(psych_PCAT[,c(2:4,33)], psych_strain_collection[,c(3,27)])
Joining, by = "strainID"
test2 = as.data.frame(table(test$strainID, test$overall_pan_cat))
colnames(test2) = c("strainID", "pan_cat", "frequency")
test2 = left_join(test2, psych_strain_collection[,c(3,27)])
Joining, by = "strainID"
number_genes_perstrain = as.data.frame(table(psych_PCAT$strainID))
colnames(number_genes_perstrain) = c("strainID", "num_genes")
test2 = left_join(test2, number_genes_perstrain)
Joining, by = "strainID"
test2$ISME_source[test2$strainID == "P_sp_IAM12030_72-O-c"] <- "terrestrial"
agg_pancat_byiso_bygenome = aggregate(test2$frequency, by = list(test2$ISME_source, test2$pan_cat), FUN = mean)
colnames(agg_pancat_byiso_bygenome) = c("isolation_source", "pan_cat", "average_geneclusters_pergenome")
agg_pancat_byiso_bygenome$pan_cat = factor(agg_pancat_byiso_bygenome$pan_cat, levels = c("core", "soft core", "shell", "cloud"))
agg_pancat_byiso_bygenome_sp = spread(agg_pancat_byiso_bygenome, key = pan_cat, value = average_geneclusters_pergenome)
chisq_pan_iso = chisq.test(agg_pancat_byiso_bygenome_sp[1:5,2:5])
corrplot(chisq_pan_iso$residuals, is.corr = F)

aggregate(test2$frequency, by = list(test2$pan_cat), FUN = mean)
    Group.1          x
1      core 1148.48235
2 soft core  449.64706
3     shell 1004.58824
4     cloud   70.36471
pancat_byiso_bygenome = as.data.frame(table(psych_PCAT$strainID, psych_PCAT$overall_pan_cat))
colnames(pancat_byiso_bygenome) = c("strainID", "pan_cat", "frequency")
pancat_byiso_bygenome = left_join(pancat_byiso_bygenome, psych_strain_collection[,c(3,27)])
Joining, by = "strainID"
pancat_byiso_bygenome = left_join(pancat_byiso_bygenome, number_genes_perstrain)
Joining, by = "strainID"
  
pancat_byiso_bygenome$ISME_source = factor(pancat_byiso_bygenome$ISME_source, 
                                           levels = c("warm host", "other host", "food", "marine", "terrestrial"))
pancat_byiso_bygenome$pan_cat = factor(pancat_byiso_bygenome$pan_cat, 
                                           levels = c("core", "soft core", "shell", "cloud"))
pancat_byiso_bygenome$ISME_source[pancat_byiso_bygenome$strainID == "P_sp_IAM12030_72-O-c"] <- "terrestrial"
pancat_byiso_bygenome_sp = spread(pancat_byiso_bygenome, key = "pan_cat", value = "frequency")
pancat_byiso_bygenome_sp$core_prop = pancat_byiso_bygenome_sp$core/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_sp$softcore_prop = pancat_byiso_bygenome_sp$`soft core`/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_sp$shell_prop = pancat_byiso_bygenome_sp$shell/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_sp$cloud_prop = pancat_byiso_bygenome_sp$cloud/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_ga = pancat_byiso_bygenome_sp %>% 
  gather(core_prop, softcore_prop, shell_prop, cloud_prop, key = "pan_cat", value = "proportion") %>%
  select(-core, -`soft core`, -shell, -cloud)
pancat_byiso_bygenome_ga$pan_cat = str_remove(pancat_byiso_bygenome_ga$pan_cat, "_prop")
pancat_byiso_bygenome_ga$pan_cat = factor(pancat_byiso_bygenome_ga$pan_cat, 
                                          levels = c("core", "softcore", "shell", "cloud"))
(genenum_iso = ggplot(data = pancat_byiso_bygenome_ga, aes(x = ISME_source, y= num_genes, color = ISME_source)) + 
  geom_boxplot(alpha = 0) + 
  geom_point(position=position_dodge(width=0.75),aes(group=ISME_source)) +
  scale_color_manual(values = ISMEcolors) +
  #facet_grid(.~pan_cat, scales = "free") +
  theme_classic())

(pan_str_iso = ggplot(data = pancat_byiso_bygenome_ga, aes(x = ISME_source, y= proportion, color = ISME_source)) + 
  geom_boxplot(alpha = 0) + 
  geom_point(position=position_dodge(width=0.75),aes(group=ISME_source)) +
  scale_color_manual(values = ISMEcolors) +
  facet_grid(.~pan_cat, scales = "free") +
  theme_classic())

stats_core = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'core')
kruskal.test(proportion ~ ISME_source, data = stats_core)

    Kruskal-Wallis rank sum test

data:  proportion by ISME_source
Kruskal-Wallis chi-squared = 18.442, df = 4, p-value = 0.001011
pairwise.wilcox.test(x = stats_core$proportion, g = stats_core$ISME_source, p.adjust.method = 'BH')

    Pairwise comparisons using Wilcoxon rank sum test 

data:  stats_core$proportion and stats_core$ISME_source 

            warm host other host food  marine
other host  0.013     -          -     -     
food        0.013     0.918      -     -     
marine      0.738     0.022      0.023 -     
terrestrial 0.918     0.013      0.014 0.745 

P value adjustment method: BH 
aggregate(stats_core$proportion, by = list(stats_core$ISME_source), FUN = median)
      Group.1         x
1   warm host 0.4424822
2  other host 0.4151218
3        food 0.4139804
4      marine 0.4430722
5 terrestrial 0.4381671
stats_softcore = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'softcore')
kruskal.test(proportion ~ ISME_source, data = stats_softcore)

    Kruskal-Wallis rank sum test

data:  proportion by ISME_source
Kruskal-Wallis chi-squared = 12.696, df = 4, p-value = 0.01286
pairwise.wilcox.test(x = stats_softcore$proportion, g = stats_softcore$ISME_source, p.adjust.method = 'BH')

    Pairwise comparisons using Wilcoxon rank sum test 

data:  stats_softcore$proportion and stats_softcore$ISME_source 

            warm host other host food  marine
other host  0.386     -          -     -     
food        0.868     0.407      -     -     
marine      0.143     0.144      0.094 -     
terrestrial 0.065     0.065      0.033 0.868 

P value adjustment method: BH 
aggregate(stats_softcore$proportion, by = list(stats_softcore$ISME_source), FUN = median)
      Group.1         x
1   warm host 0.1645282
2  other host 0.1670198
3        food 0.1651576
4      marine 0.1757599
5 terrestrial 0.1745093
stats_shell = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'shell')
kruskal.test(proportion ~ ISME_source, data = stats_shell)

    Kruskal-Wallis rank sum test

data:  proportion by ISME_source
Kruskal-Wallis chi-squared = 17.722, df = 4, p-value = 0.001398
pairwise.wilcox.test(x = stats_shell$proportion, g = stats_shell$ISME_source, p.adjust.method = 'BH')
cannot compute exact p-value with ties

    Pairwise comparisons using Wilcoxon rank sum test 

data:  stats_shell$proportion and stats_shell$ISME_source 

            warm host other host food  marine
other host  0.023     -          -     -     
food        0.035     0.984      -     -     
marine      0.984     0.013      0.034 -     
terrestrial 0.984     0.020      0.034 0.984 

P value adjustment method: BH 
aggregate(stats_shell$proportion, by = list(stats_shell$ISME_source), FUN = median)
      Group.1         x
1   warm host 0.3662383
2  other host 0.4024303
3        food 0.3940601
4      marine 0.3621622
5 terrestrial 0.3669571
stats_cloud = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'cloud')
kruskal.test(proportion ~ ISME_source, data = stats_cloud)

    Kruskal-Wallis rank sum test

data:  proportion by ISME_source
Kruskal-Wallis chi-squared = 7.691, df = 4, p-value = 0.1036
pairwise.wilcox.test(x = stats_cloud$proportion, g = stats_cloud$ISME_source, p.adjust.method = 'BH')
cannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with ties

    Pairwise comparisons using Wilcoxon rank sum test 

data:  stats_cloud$proportion and stats_cloud$ISME_source 

            warm host other host food marine
other host  0.20      -          -    -     
food        0.87      0.20       -    -     
marine      0.96      0.21       0.65 -     
terrestrial 0.96      0.27       0.65 0.96  

P value adjustment method: BH 
aggregate(stats_cloud$proportion, by = list(stats_cloud$ISME_source), FUN = median)
      Group.1          x
1   warm host 0.01546329
2  other host 0.01047651
3        food 0.02462527
4      marine 0.01698842
5 terrestrial 0.02118343

Figure 3.4 unique gene content between isolation sources

warm = psych_strain_collection %>% filter(ISME_source == "warm host") %>% select(strainID) %>% unlist() %>% unname()
other = psych_strain_collection %>% filter(ISME_source == "other host") %>% select(strainID) %>% unlist() %>% unname()
food = psych_strain_collection %>% filter(ISME_source == "food") %>% select(strainID) %>% unlist() %>% unname()
marine = psych_strain_collection %>% filter(ISME_source == "marine") %>% select(strainID) %>% unlist() %>% unname()
terrestrial = psych_strain_collection %>% filter(ISME_source == "terrestrial") %>% select(strainID) %>% unlist() %>% unname()
warmseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% warm])
names(warmseqsum) = rownames(genes_mat)
warmisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% warm])
names(warmisosum) = rownames(genes_mat)
otherseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% other])
names(otherseqsum) = rownames(genes_mat)
otherisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% other])
names(otherisosum) = rownames(genes_mat)
foodseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% food])
names(otherseqsum) = rownames(genes_mat)
foodisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% food])
names(foodisosum) = rownames(genes_mat)
marineseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% marine])
names(marineseqsum) = rownames(genes_mat)
marineisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% marine])
names(marineisosum) = rownames(genes_mat)
terrestrialseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% terrestrial])
names(terrestrialseqsum) = rownames(genes_mat)
terrestrialisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% terrestrial])
names(terrestrialisosum) = rownames(genes_mat)
allseqsum = rowSums(genes_mat_numbered)
names(allseqsum) = rownames(genes_mat)
allisosum = rowSums(genematrix_binary)
names(allisosum) = rownames(genes_mat)
pan_str_iso = rbind(allseqsum, allisosum, warmseqsum, warmisosum, otherseqsum, otherisosum, 
                                       foodseqsum, foodisosum, marineseqsum, marineisosum, terrestrialseqsum, terrestrialisosum)
pan_str_iso = t(pan_str_iso)
pan_str_iso = as.data.frame(pan_str_iso)
pan_str_iso$geneCluster = rownames(pan_str_iso)
pan_str_iso$overall_pan_cat = NA
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum >= 84] <- "core"
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum <= 83 & pan_str_iso$allisosum >= 77] <- "soft core"
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum <= 82 & pan_str_iso$allisosum >= 2 ] <- "shell"
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum == 1 ] <- "cloud"
pan_str_iso$warm_pan_cat = NA
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum >= 13] <- "core"
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum <= 12 & pan_str_iso$warmisosum >= 2 ] <- "shell"
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum == 1 ] <- "cloud"
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum == 0 ] <- "absent"
pan_str_iso$other_pan_cat = NA
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum >= 20] <- "core"
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum <= 19 & pan_str_iso$otherisosum >= 2 ] <- "shell"
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum == 1 ] <- "cloud"
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum == 0 ] <- "absent"
pan_str_iso$food_pan_cat = NA
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum == 11] <- "core"
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum <= 10 & pan_str_iso$foodisosum >= 2 ] <- "shell"
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum == 1 ] <- "cloud"
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum == 0 ] <- "absent"
pan_str_iso$marine_pan_cat = NA
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum >= 19] <- "core"
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum <= 18 & pan_str_iso$marineisosum >= 2 ] <- "shell"
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum == 1 ] <- "cloud"
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum == 0 ] <- "absent"
pan_str_iso$terrestrial_pan_cat = NA
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum >= 15] <- "core"
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum <= 14 & pan_str_iso$terrestrialisosum >= 2 ] <- "shell"
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum == 1 ] <- "cloud"
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum == 0 ] <- "absent"
unique_warm = pan_str_iso %>% filter(warm_pan_cat != "absent" & other_pan_cat == "absent" & food_pan_cat == "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_warm_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_warm)
unique_warm_annot$isolation = "warm"
unique_other = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat != "absent" & food_pan_cat == "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_other_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_other)
unique_other_annot$isolation = "other"
unique_food = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat == "absent" & food_pan_cat != "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_food_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_food)
unique_food_annot$isolation = "food"
unique_marine = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat == "absent" & food_pan_cat == "absent" & marine_pan_cat != "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_marine_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_marine)
unique_marine_annot$isolation = "marine"
unique_terrestrial = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat == "absent" & food_pan_cat == "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat != "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_terrestrial_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_terrestrial)
unique_terrestrial_annot$isolation = "terrestrial"
unique_genes_per_isolation = rbind(unique_warm_annot, unique_other_annot, unique_food_annot, unique_marine_annot, unique_terrestrial_annot)
unique_genes_per_isolation$isolation = factor(unique_genes_per_isolation$isolation, levels = c("warm", "other", "food", "marine", "terrestrial"))
unique_genes_per_isolation$COG_category = factor(unique_genes_per_isolation$COG_category, levels = c("D", "M", "N", "O", "T", "U", "V", "W", "Z",
                                                                                 "A", "B","J", "K", "L",
                                                                                 "C", "E", "F", "G", "H", "I", "P", "Q",
                                                                                 "S", "X"))
cols = c("#72300C", "#93074D", "#D60D69", "#ED5FA6", "#CE1B13", "#A50505",
         "#E5530A", "#E2770C", "#F9A138", "#F7F072", "#F7D239", "#CCF43B",
         "#5BB73D", "#057F28", "#05541A", "#20998B", "#60B1F4", "#1458F2",
         "#5141AF", "#1F048E", "#310872", "#520775", "#9F9FA0", "#39383A")
(unique_gene_content = ggplot(data = unique_genes_per_isolation, aes(x = isolation, fill = COG_category)) +
  geom_bar(stat = 'count') +
  scale_fill_manual(values = cols) +
  theme_classic())

unique_corr = table(unique_genes_per_isolation$isolation, unique_genes_per_isolation$COG_category)
chisq_uni_iso = chisq.test(unique_corr)
Chi-squared approximation may be incorrect
corrplot(chisq_uni_iso$residuals, is.corr = F)

#add unique_gene_content w corrplot in illustrator

Pan-genome wide association study with isolation source as trait.

unique(psych_strain_collection$ISME_source)
[1] "marine"      "food"        "terrestrial" "warm host"   "other host" 
isolationsource = psych_strain_collection[,c(3,27)]
isolationsource$dummy = 1
isolationsource = isolationsource %>%
  spread(key = ISME_source, value = dummy)
isolationsource[is.na(isolationsource)] <- 0
rownames(isolationsource) = isolationsource$strainID
WH <- as.vector(unlist(isolationsource$`warm host`))
names(WH) = rownames(isolationsource)
all(names(WH) %in% rownames(genematrix_treewas))
[1] TRUE
all(rownames(genematrix_treewas) %in% names(WH))
[1] TRUE
WH_treewas <- treeWAS(snps = genematrix_treewas, phen = WH, tree = P.tree.noout, seed = 1)
[1] "treeWAS snps sim done @ 2021-03-24 10:11:57"
[1] "Reconstructions completed @ 2021-03-24 10:12:10"
[1] "Started running terminal test @ 2021-03-24 10:12:10"
[1] "Real data scores completed for terminal test @ 2021-03-24 10:12:10"
[1] "Simulated data scores completed for terminal test @ 2021-03-24 10:12:10"
[1] "Started running simultaneous test @ 2021-03-24 10:12:10"
[1] "Real data scores completed for simultaneous test @ 2021-03-24 10:12:10"
[1] "Simulated data scores completed for simultaneous test @ 2021-03-24 10:12:11"
[1] "Started running subsequent test @ 2021-03-24 10:12:11"
[1] "Real data scores completed for subsequent test @ 2021-03-24 10:12:11"
[1] "Simulated data scores completed for subsequent test @ 2021-03-24 10:12:11"
[1] "Finished running terminal test @ 2021-03-24 10:12:12"
[1] "Finished running simultaneous test @ 2021-03-24 10:12:12"
[1] "Finished running subsequent test @ 2021-03-24 10:12:12"
[1] "ID of significant loci completed @ 2021-03-24 10:12:12"

print(WH_treewas)
    #################### 
    ## treeWAS output ## 
    #################### 
     
    #################### 
    ## All findings:  ## 
    #################### 
Number of significant loci: [1] 0
     
    ######################## 
    ## Findings by test:  ## 
    ######################## 
     ####################  
     ##  terminal test ## 
     ####################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
0.8588235 
     ########################  
     ##  simultaneous test ## 
     ########################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
      4.5 
     ######################  
     ##  subsequent test ## 
     ######################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
 159.3333 
OH <- as.vector(unlist(isolationsource$`other host`))
names(OH) = rownames(isolationsource)
all(names(OH) %in% rownames(genematrix_treewas))
[1] TRUE
all(rownames(genematrix_treewas) %in% names(OH))
[1] TRUE
OH_treewas <- treeWAS(snps = genematrix_treewas, phen = OH, tree = P.tree.noout, seed = 1)
[1] "treeWAS snps sim done @ 2021-03-24 10:12:20"
[1] "Reconstructions completed @ 2021-03-24 10:12:31"
[1] "Started running terminal test @ 2021-03-24 10:12:31"
[1] "Real data scores completed for terminal test @ 2021-03-24 10:12:31"
[1] "Simulated data scores completed for terminal test @ 2021-03-24 10:12:32"
[1] "Started running simultaneous test @ 2021-03-24 10:12:32"
[1] "Real data scores completed for simultaneous test @ 2021-03-24 10:12:32"
[1] "Simulated data scores completed for simultaneous test @ 2021-03-24 10:12:32"
[1] "Started running subsequent test @ 2021-03-24 10:12:32"
[1] "Real data scores completed for subsequent test @ 2021-03-24 10:12:33"
[1] "Simulated data scores completed for subsequent test @ 2021-03-24 10:12:33"
[1] "Finished running terminal test @ 2021-03-24 10:12:33"
[1] "Finished running simultaneous test @ 2021-03-24 10:12:33"
[1] "Finished running subsequent test @ 2021-03-24 10:12:34"
[1] "ID of significant loci completed @ 2021-03-24 10:12:34"

print(OH_treewas)
    #################### 
    ## treeWAS output ## 
    #################### 
     
    #################### 
    ## All findings:  ## 
    #################### 
Number of significant loci: [1] 5
Significant loci: 
[1] "GC00002129_r1_3" "GC00002231_r1_1" "GC00002376"      "GC00002380_r1_2"
[5] "GC00002622"     
     
    ######################## 
    ## Findings by test:  ## 
    ######################## 
     ####################  
     ##  terminal test ## 
     ####################  
Number of significant loci: 
[1] 1
Significance threshold: 
99.99997% 
0.6235294 
Significant loci: 
           SNP.locus p.value     score G1P1 G0P0 G1P0 G0P1
GC00002622      6994       0 0.6705882   11   60    2   12
     ########################  
     ##  simultaneous test ## 
     ########################  
Number of significant loci: 
[1] 4
Significance threshold: 
99.99997% 
        4 
Significant loci: 
                SNP.locus p.value score G1P1 G0P0 G1P0 G0P1
GC00002129_r1_3      5957       0     6    9   56    6   14
GC00002231_r1_1      6198       0     5   11   53    9   12
GC00002376           6521       0     5    6   52   10   17
GC00002380_r1_2      6531       0     6   10   56    6   13
     ######################  
     ##  subsequent test ## 
     ######################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
 138.2667 
Fo <- as.vector(unlist(isolationsource$food))
names(Fo) = rownames(isolationsource)
all(names(Fo) %in% rownames(genematrix_treewas))
[1] TRUE
all(rownames(genematrix_treewas) %in% names(Fo))
[1] TRUE
Fo_treewas <- treeWAS(snps = genematrix_treewas, phen = Fo, tree = P.tree.noout, seed = 1)
[1] "treeWAS snps sim done @ 2021-03-24 10:12:41"
[1] "Reconstructions completed @ 2021-03-24 10:12:53"
[1] "Started running terminal test @ 2021-03-24 10:12:53"
[1] "Real data scores completed for terminal test @ 2021-03-24 10:12:53"
[1] "Simulated data scores completed for terminal test @ 2021-03-24 10:12:54"
[1] "Started running simultaneous test @ 2021-03-24 10:12:54"
[1] "Real data scores completed for simultaneous test @ 2021-03-24 10:12:54"
[1] "Simulated data scores completed for simultaneous test @ 2021-03-24 10:12:54"
[1] "Started running subsequent test @ 2021-03-24 10:12:54"
[1] "Real data scores completed for subsequent test @ 2021-03-24 10:12:54"
[1] "Simulated data scores completed for subsequent test @ 2021-03-24 10:12:54"
[1] "Finished running terminal test @ 2021-03-24 10:12:55"
[1] "Finished running simultaneous test @ 2021-03-24 10:12:55"
[1] "Finished running subsequent test @ 2021-03-24 10:12:55"
[1] "ID of significant loci completed @ 2021-03-24 10:12:55"

print(Fo_treewas)
    #################### 
    ## treeWAS output ## 
    #################### 
     
    #################### 
    ## All findings:  ## 
    #################### 
Number of significant loci: [1] 5
Significant loci: 
[1] "GC00000256_r1_r1_1_p1" "GC00002564"            "GC00002565"           
[4] "GC00002566"            "GC00002666"           
     
    ######################## 
    ## Findings by test:  ## 
    ######################## 
     ####################  
     ##  terminal test ## 
     ####################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
0.8580392 
     ########################  
     ##  simultaneous test ## 
     ########################  
Number of significant loci: 
[1] 5
Significance threshold: 
99.99997% 
 4.966667 
Significant loci: 
                      SNP.locus p.value score G1P1 G0P0 G1P0 G0P1
GC00000256_r1_r1_1_p1      1711       0     5    7   53   21    4
GC00002564                 6889       0     5    5   67    7    6
GC00002565                 6890       0     5    5   67    7    6
GC00002566                 6891       0     5    5   67    7    6
GC00002666                 7085       0     5    5   68    6    6
     ######################  
     ##  subsequent test ## 
     ######################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
 159.9889 
Ma <- as.vector(unlist(isolationsource$marine))
names(Ma) = rownames(isolationsource)
all(names(Ma) %in% rownames(genematrix_treewas))
[1] TRUE
all(rownames(genematrix_treewas) %in% names(Ma))
[1] TRUE
Ma_treewas <- treeWAS(snps = genematrix_treewas, phen = Ma, tree = P.tree.noout, seed = 1)
[1] "treeWAS snps sim done @ 2021-03-24 10:13:02"
[1] "Reconstructions completed @ 2021-03-24 10:13:14"
[1] "Started running terminal test @ 2021-03-24 10:13:14"
[1] "Real data scores completed for terminal test @ 2021-03-24 10:13:14"
[1] "Simulated data scores completed for terminal test @ 2021-03-24 10:13:15"
[1] "Started running simultaneous test @ 2021-03-24 10:13:15"
[1] "Real data scores completed for simultaneous test @ 2021-03-24 10:13:15"
[1] "Simulated data scores completed for simultaneous test @ 2021-03-24 10:13:15"
[1] "Started running subsequent test @ 2021-03-24 10:13:15"
[1] "Real data scores completed for subsequent test @ 2021-03-24 10:13:15"
[1] "Simulated data scores completed for subsequent test @ 2021-03-24 10:13:15"
[1] "Finished running terminal test @ 2021-03-24 10:13:16"
[1] "Finished running simultaneous test @ 2021-03-24 10:13:16"
[1] "Finished running subsequent test @ 2021-03-24 10:13:16"
[1] "ID of significant loci completed @ 2021-03-24 10:13:16"

print(Ma_treewas)
    #################### 
    ## treeWAS output ## 
    #################### 
     
    #################### 
    ## All findings:  ## 
    #################### 
Number of significant loci: [1] 4
Significant loci: 
[1] "GC00001426_2" "GC00002047"   "GC00002181_2" "GC00003144"  
     
    ######################## 
    ## Findings by test:  ## 
    ######################## 
     ####################  
     ##  terminal test ## 
     ####################  
Number of significant loci: 
[1] 2
Significance threshold: 
99.99997% 
0.6227451 
Significant loci: 
             SNP.locus p.value      score G1P1 G0P0 G1P0 G0P1
GC00001426_2      4122       0 -0.6235294   15    1   63    6
GC00003144        7862       0  0.6235294    6   63    1   15
     ########################  
     ##  simultaneous test ## 
     ########################  
Number of significant loci: 
[1] 2
Significance threshold: 
99.99997% 
        5 
Significant loci: 
             SNP.locus p.value score G1P1 G0P0 G1P0 G0P1
GC00002047        5782       0    11   13   49   15    8
GC00002181_2      6087       0     8   11   52   12   10
     ######################  
     ##  subsequent test ## 
     ######################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
 143.6667 
Te <- as.vector(unlist(isolationsource$terrestrial))
names(Te) = rownames(isolationsource)
all(names(Te) %in% rownames(genematrix_treewas))
[1] TRUE
all(rownames(genematrix_treewas) %in% names(Te))
[1] TRUE
Te_treewas <- treeWAS(snps = genematrix_treewas, phen = Te, tree = P.tree.noout, seed = 1)
[1] "treeWAS snps sim done @ 2021-03-24 10:13:23"
[1] "Reconstructions completed @ 2021-03-24 10:13:36"
[1] "Started running terminal test @ 2021-03-24 10:13:36"
[1] "Real data scores completed for terminal test @ 2021-03-24 10:13:36"
[1] "Simulated data scores completed for terminal test @ 2021-03-24 10:13:37"
[1] "Started running simultaneous test @ 2021-03-24 10:13:37"
[1] "Real data scores completed for simultaneous test @ 2021-03-24 10:13:37"
[1] "Simulated data scores completed for simultaneous test @ 2021-03-24 10:13:37"
[1] "Started running subsequent test @ 2021-03-24 10:13:37"
[1] "Real data scores completed for subsequent test @ 2021-03-24 10:13:38"
[1] "Simulated data scores completed for subsequent test @ 2021-03-24 10:13:38"
[1] "Finished running terminal test @ 2021-03-24 10:13:38"
[1] "Finished running simultaneous test @ 2021-03-24 10:13:38"
[1] "Finished running subsequent test @ 2021-03-24 10:13:38"
[1] "ID of significant loci completed @ 2021-03-24 10:13:38"

print(Te_treewas)
    #################### 
    ## treeWAS output ## 
    #################### 
     
    #################### 
    ## All findings:  ## 
    #################### 
Number of significant loci: [1] 1
Significant loci: 
[1] "GC00000111_6"
     
    ######################## 
    ## Findings by test:  ## 
    ######################## 
     ####################  
     ##  terminal test ## 
     ####################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
0.7647059 
     ########################  
     ##  simultaneous test ## 
     ########################  
Number of significant loci: 
[1] 1
Significance threshold: 
99.99997% 
 4.966667 
Significant loci: 
             SNP.locus p.value score G1P1 G0P0 G1P0 G0P1
GC00000111_6      1141       0    -5    6   15   54   10
     ######################  
     ##  subsequent test ## 
     ######################  
Number of significant loci: 
[1] 0
Significance threshold: 
99.99997% 
 153.6667 
OH_treewas_gc = OH_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
OH_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% OH_treewas_gc)
Fo_treewas_gc = Fo_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
Fo_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% Fo_treewas_gc)
Ma_treewas_gc = Ma_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
Ma_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% Ma_treewas_gc)
Te_treewas_gc = Te_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
Te_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% Te_treewas_gc)

Isolation source ancestral character estimation.

#two state ancestral state reconstruction
ordered_df = strain_collection[match(M.tree$tip.label, strain_collection$strainID),]
rownames(ordered_df) = ordered_df$strainID
x1<-setNames(ordered_df[,21],rownames(ordered_df))
x1col = c("deepskyblue3", "orange")
fitER1 = ace(x1, M.tree, model = "ER", type = "discrete", marginal = TRUE)
fitSYM1 = ace(x1, M.tree, model = "SYM", type = "discrete", marginal = TRUE)
fitARD1 = ace(x1, M.tree, model = "ARD", type = "discrete", marginal = TRUE)
#ARD model maximizes likelihood, but does it do so significantly?
1 - pchisq(2*abs(fitARD1$loglik - fitER1$loglik), 1) #p = 0.15
[1] 0.1544003
1 - pchisq(2*abs(fitARD1$loglik - fitSYM1$loglik), 1) #p = 0.15
[1] 0.1544003
#ok, so is there a difference between ER and SYM?
1 - pchisq(2*abs(fitER1$loglik - fitSYM1$loglik), 1)
[1] 1
#no
#so I will go with the simplest ER model
plotTree(M.tree,fsize=0.8,ftype="i")
nodelabels(node=1:M.tree$Nnode+Ntip(M.tree),pie=fitER1$lik.anc,piecol = x1col,cex=0.5)
#tiplabels(pie=to.matrix(x1[M.tree$tip.label],levels(x1)),piecol = x1col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x1)),colors=x1col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(M.tree)),fsize=0.8)
#plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x1,sort(unique(x1))),piecol=x1col,cex=0.3)
add.simmap.legend(colors=x1col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(M.tree)),fsize=0.8)

fitER1

    Ancestral Character Estimation

Call: ace(x = x1, phy = M.tree, type = "discrete", model = "ER", marginal = TRUE)

    Log-likelihood: -39.5478 

Rate index matrix:
  c w
c . 1
w 1 .

Parameter estimates:
 rate index estimate std-err
          1   1.4862  0.4245

Scaled likelihoods at the root (type '...$lik.anc' to get them for all nodes):
        c         w 
0.1730664 0.8269336 
psychroot = drop.tip(M.tree, c("M_boevrei","M_atlantae","M_osloensis","A_puyangensis",
                     "M_cuniculi","M_porci","M_pluranimalium","M_canis","M_catarrhalis","M_caviae",
                     "M_ovis","M_bovoculi","M_oblonga","M_equi","M_bovis","M_caprae","M_lacunata","M_nonliquefaciens"))
x1psychroot<-x1[names(x1) %in% psychroot$tip.label]
fitER_psychroot = ace(x1psychroot, psychroot, model = "ER", type = "discrete", marginal = TRUE)
fitER_psychroot

    Ancestral Character Estimation

Call: ace(x = x1psychroot, phy = psychroot, type = "discrete", model = "ER", 
    marginal = TRUE)

    Log-likelihood: -32.31518 

Rate index matrix:
  c w
c . 1
w 1 .

Parameter estimates:
 rate index estimate std-err
          1   2.7609  0.7641

Scaled likelihoods at the root (type '...$lik.anc' to get them for all nodes):
        c         w 
0.4464888 0.5535112 
plotTree(psychroot,fsize=0.8,ftype="i")
nodelabels(node=1:psychroot$Nnode+Ntip(psychroot),pie=fitER_psychroot$lik.anc,piecol = x1col,cex=0.5)
#tiplabels(pie=to.matrix(x1psychroot[psychroot$tip.label],levels(x1psychroot)),piecol = x1col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x1psychroot)),colors=x1col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(psychroot)),fsize=0.8)
#plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x1psychroot,sort(unique(x1psychroot))),piecol=x1col,cex=0.3)
add.simmap.legend(colors=x1col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(psychroot)),fsize=0.8)

#now by more specific isolation sources 
ordered_df$ISME_source = factor(ordered_df$ISME_source,
                                levels = c("warm host", "other host", "food", "marine", "terrestrial"))
x2<-setNames(ordered_df[,19],rownames(ordered_df))
x2col = c("#F9D503", "#5DC863", "#21908C", "#3B528B", "#440154")
fitER2 = ace(x2, M.tree, model = "ER", type = "discrete", marginal = TRUE)
fitSYM2 = ace(x2, M.tree, model = "SYM", type = "discrete", marginal = TRUE)
NaNs producedNA/Inf replaced by maximum positive valueNaNs produced
fitARD2 = ace(x2, M.tree, model = "ARD", type = "discrete", marginal = TRUE)
imaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionimaginary parts discarded in coercionNaNs producedNA/Inf replaced by maximum positive valueNaNs produced
#ARD model maximizes likelihood, but does it do so significantly?
1 - pchisq(2*abs(fitARD2$loglik - fitER2$loglik), 1) #p = 1.110223e-15
[1] 1.110223e-15
1 - pchisq(2*abs(fitARD2$loglik - fitSYM2$loglik), 1) #p = 4.260376e-05
[1] 4.260376e-05
#both significant, so
#I will go with the ARD model
1 - pchisq(2*abs(fitSYM2$loglik - fitER2$loglik), 1)
[1] 5.422884e-12
plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x2,sort(unique(x2))),piecol=x2col,cex=0.3)
add.simmap.legend(colors=x2col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(M.tree)),fsize=0.8)
#plotTree(M.tree,fsize=0.8,ftype="i")
nodelabels(node=1:M.tree$Nnode+Ntip(M.tree),pie=fitER2$lik.anc,piecol = x2col,cex=0.5)

#tiplabels(pie=to.matrix(x2[M.tree$tip.label],levels(x2)),piecol = x2col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x2)),colors=x2col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(M.tree)),fsize=0.8)
x2psychroot<-x2[names(x2) %in% psychroot$tip.label]
fitER2_psychroot = ace(x2psychroot, psychroot, model = "ER", type = "discrete", marginal = TRUE)
fitER2_psychroot

    Ancestral Character Estimation

Call: ace(x = x2psychroot, phy = psychroot, type = "discrete", model = "ER", 
    marginal = TRUE)

    Log-likelihood: -126.5058 

Rate index matrix:
            warm host other host food marine terrestrial
warm host           .          1    1      1           1
other host          1          .    1      1           1
food                1          1    .      1           1
marine              1          1    1      .           1
terrestrial         1          1    1      1           .

Parameter estimates:
 rate index estimate std-err
          1  14.7976  3.0498

Scaled likelihoods at the root (type '...$lik.anc' to get them for all nodes):
  warm host  other host        food      marine terrestrial 
        0.2         0.2         0.2         0.2         0.2 
plotTree(psychroot,fsize=0.8,ftype="i")
nodelabels(node=1:psychroot$Nnode+Ntip(psychroot),pie=fitER2_psychroot$lik.anc,piecol = x2col,cex=0.5)
#tiplabels(pie=to.matrix(x2psychroot[psychroot$tip.label],levels(x1psychroot)),piecol = x2col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x2psychroot)),colors=x2col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(psychroot)),fsize=0.8)
#plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x2psychroot,sort(unique(x2psychroot))),piecol=x2col,cex=0.3)
add.simmap.legend(colors=x2col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(psychroot)),fsize=0.8)

Predicted cold-adapted protein traits.

Figure 3.6

psych_genecluster_summ = as.data.frame(table(psych_PCAT$geneCluster))
colnames(psych_genecluster_summ) = c("geneCluster", "frequency")
highpcatfreq = as.data.frame(table(psych_highPCAT$geneCluster))
colnames(highpcatfreq) = c("geneCluster", "freq_highPCAT")
psych_genecluster_summ = left_join(psych_genecluster_summ, highpcatfreq)
Joining, by = "geneCluster"
psych_genecluster_summ$freq_highPCAT[is.na(psych_genecluster_summ$freq_highPCAT) == TRUE] <- 0
psych_genecluster_summ$perc_highPCAT = psych_genecluster_summ$freq_highPCAT/psych_genecluster_summ$frequency
psych_genecluster_summ_nosingletons = psych_genecluster_summ %>% filter(frequency != 1)
(perHPCAT = ggplot(data = psych_genecluster_summ, aes(x = perc_highPCAT)) + geom_histogram() + theme_classic() + xlab("Percentage of CDS in geneCluster that are classified as 'highly cold adaptive'"))

(perHPCAT_noS = ggplot(data = psych_genecluster_summ_nosingletons, aes(x = perc_highPCAT)) + geom_histogram() + theme_classic() + xlab("Percentage of CDS in geneCluster that are classified as 'highly cold adaptive', singletons removed"))

ggarrange(perHPCAT, perHPCAT_noS, align = 'h', labels = c("A", "B"))
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Figure 3.7

psych_PCAT_strainID_summ = as.data.frame(table(psych_PCAT$strainID))
colnames(psych_PCAT_strainID_summ) = c("strainID", "frequency")
highpcat_bystrain = as.data.frame(table(psych_highPCAT$strainID))
colnames(highpcat_bystrain) = c("strainID", "freq_highPCAT")
psych_PCAT_strainID_summ = left_join(psych_PCAT_strainID_summ, highpcat_bystrain)
Joining, by = "strainID"
psych_PCAT_strainID_summ$perc_highPCAT = psych_PCAT_strainID_summ$freq_highPCAT/psych_PCAT_strainID_summ$frequency
perc_highPCAT = psych_PCAT_strainID_summ$perc_highPCAT
names(perc_highPCAT) = psych_PCAT_strainID_summ$strainID
setdiff(names(perc_highPCAT), P.tree.noout$tip.label)
[1] "P_sp_IAM12030_72-O-c"
phylo_PCAT = phylosig(tree = P.tree.noout, x = perc_highPCAT, test = TRUE)
[1] "some species in x are missing from tree, dropping missing taxa from x"
[1] "some species in tree are missing from x , dropping missing taxa from the tree"
phylo_PCAT

Phylogenetic signal K : 0.456351 
P-value (based on 1000 randomizations) : 0.001 
(HPCAT_bystrain = ggplot(data = psych_PCAT_strainID_summ, aes(x = perc_highPCAT)) + geom_histogram() + theme_classic() + xlab("Percentage of highly cold adaptive proteins by strain"))

psych_genome_summaries = left_join(psych_genome_summaries, psych_strain_collection[,c(3,5,21,27)])
Joining, by = "strain_plus_code"
psych_PCAT_strainID_summ$strainID = as.character(psych_PCAT_strainID_summ$strainID)
psych_PCAT_strainID_summ$strainID[psych_PCAT_strainID_summ$strainID == "P_sp_IAM12030_72-O-c"] <- "P_sp_IAM12030-72-O-c"
psych_PCAT_strainID_summ = left_join(psych_PCAT_strainID_summ, psych_genome_summaries[,c(19,5,20,21)])
Joining, by = "strainID"
psych_PCAT_strainID_summ$temprange = "mesophile"
psych_PCAT_strainID_summ$temprange[psych_PCAT_strainID_summ$strainID == "P_frigidicola_ACAM304" |
                                     psych_PCAT_strainID_summ$strainID == "P_frigidicola_ACAM309" |
                                     psych_PCAT_strainID_summ$strainID == "P_sp_72-O-c" |
                                     psych_PCAT_strainID_summ$strainID == "P_sp_IAM12030-72-O-c" |
                                     psych_PCAT_strainID_summ$strainID == "P_urativorans_ACAM311"] <- "psychrophile"
wilcox.test(perc_highPCAT ~ temprange, data = psych_PCAT_strainID_summ)

    Wilcoxon rank sum test with continuity correction

data:  perc_highPCAT by temprange
W = 256, p-value = 0.2999
alternative hypothesis: true location shift is not equal to 0
kruskal.test(frequency ~ ISME_source, data = psych_PCAT_strainID_summ)

    Kruskal-Wallis rank sum test

data:  frequency by ISME_source
Kruskal-Wallis chi-squared = 16.388, df = 4, p-value = 0.00254
kruskal.test(Genome.size..bp. ~ ISME_source, data = psych_PCAT_strainID_summ)

    Kruskal-Wallis rank sum test

data:  Genome.size..bp. by ISME_source
Kruskal-Wallis chi-squared = 14.093, df = 4, p-value = 0.007005
(genes_by_isosource = ggplot(data = psych_PCAT_strainID_summ, aes(x = ISME_source, y = frequency)) + 
  geom_boxplot(alpha = 0, aes(color = ISME_source)) + 
    geom_point(aes(color = ISME_source)) +
    scale_color_manual(values = ISMEcolors) +
    geom_signif(comparisons = list(c("warm host", "other host"), c("warm host", "food"), c("other host", "marine"), c("other host", "terrestrial"), c("food", "marine"), c("food", "terrestrial")), map_signif_level = T) +
  theme_classic() +
    ylab("# of genes per genome"))

#all pairwise comparisons:
#comparisons = split(t(combn(levels(psych_PCAT_strainID_summ$ISME_source), 2)), seq(nrow(t(combn(levels(psych_PCAT_strainID_summ$ISME_source), 2)))))
pairwise.wilcox.test(x = psych_PCAT_strainID_summ$Genome.size..bp., g = psych_PCAT_strainID_summ$ISME_source, p.adjust.method = 'BH')

    Pairwise comparisons using Wilcoxon rank sum test 

data:  psych_PCAT_strainID_summ$Genome.size..bp. and psych_PCAT_strainID_summ$ISME_source 

            food  marine other host terrestrial
marine      0.093 -      -          -          
other host  0.951 0.046  -          -          
terrestrial 0.031 0.951  0.031      -          
warm host   0.031 0.951  0.031      0.951      

P value adjustment method: BH 
pairwise.wilcox.test(x = psych_PCAT_strainID_summ$frequency, g = psych_PCAT_strainID_summ$ISME_source, p.adjust.method = 'BH')
cannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with ties

    Pairwise comparisons using Wilcoxon rank sum test 

data:  psych_PCAT_strainID_summ$frequency and psych_PCAT_strainID_summ$ISME_source 

            food  marine other host terrestrial
marine      0.046 -      -          -          
other host  1.000 0.032  -          -          
terrestrial 0.034 0.913  0.032      -          
warm host   0.021 0.655  0.021      0.913      

P value adjustment method: BH 
(HCA_by_temprange = ggplot(data = psych_PCAT_strainID_summ, aes(x = temprange, y = perc_highPCAT)) + 
    geom_boxplot(alpha = 0) +
    theme_classic() +
    ylab("% of genes that are highly cold adapted"))

psych_PCAT_strainID_summ$ISME_source = factor(psych_PCAT_strainID_summ$ISME_source, levels = c("warm host", "other host", "food", "marine", "terrestrial"))
(HCA_by_isosource = ggplot(data = psych_PCAT_strainID_summ, aes(x = ISME_source, y = perc_highPCAT)) + 
  geom_boxplot(alpha = 0, aes(color = ISME_source)) + 
    geom_point(aes(color = ISME_source)) +
    scale_color_manual(values = ISMEcolors) +
  theme_classic() +
    ylab("% of genes that are highly cold adapted"))

kruskal.test(perc_highPCAT ~ ISME_source, data = psych_PCAT_strainID_summ)

    Kruskal-Wallis rank sum test

data:  perc_highPCAT by ISME_source
Kruskal-Wallis chi-squared = 8.5337, df = 4, p-value = 0.07387
ggarrange(HCA_by_temprange, HCA_by_isosource, labels = c("A", "B"), widths = c(1, 3))

figure 3.8

highPCATlocus = psych_highPCAT %>% select(locus_tag) %>% unlist() %>% unname()
psych_PCAT$PCAT = NA
psych_PCAT$PCAT[psych_PCAT$locus_tag %in% highPCATlocus] <- "high"
psych_PCAT$PCAT[is.na(psych_PCAT$PCAT) == TRUE] <- "not"
psych_PCAT$PCAT = factor(psych_PCAT$PCAT, levels = c("not", "high"))
psych_PCAT_annot = left_join(psych_PCAT, multiCOG_genes_corrected)
Joining, by = "geneCluster"
(function_PCAT = ggplot(data = psych_PCAT_annot, 
                          aes(x = PCAT, fill = COG_category)) + 
    geom_bar(stat = 'count', position = 'fill') + 
    scale_fill_manual(values = COGcols) +
    theme_classic())

COG_PCAT_chi = chisq.test(t(table(psych_PCAT_annot$PCAT, psych_PCAT_annot$COG_category)))
COG_PCAT_chi

    Pearson's Chi-squared test

data:  t(table(psych_PCAT_annot$PCAT, psych_PCAT_annot$COG_category))
X-squared = 3750.4, df = 23, p-value < 2.2e-16
COG_PCAT_cor = corrplot(COG_PCAT_chi$residuals, is.cor = FALSE)

#arrange function_PCAT w corrplot in illustrator
---
title: "Thesis Chapter 3 Analysis"
output: html_notebook
---

Chapter 3 Abstract: 
Ecological niche can drive the genomic diversification of closely related bacterial species. Here, I apply genomic analyses to the genus  Psychrobacter,  known for its particularly wide ecological distribution. I performed a pan-genome analysis, microbial pan-genome wide association, ancestral character estimations, and analysis of selection on protein sequences of 85 strains of  Psychrobacter  to clarify the interactions between isolation source and Psychrobacter  genome evolution. There is some evidence that Psychrobacter isolation source correlates with available gene pool; each isolation source - from warm-bodied hosts such as mammals or birds, other hosts like fish or invertebrates, to food processing, marine, or terrestrial environments - is correlated with unique genes from different COG categories. Strains isolated from invertebrate and fish hosts, as well as food processing environments, have higher numbers of total genes, though fewer unique genes than strains from mammals, birds, or marine environments. After accounting for population structure, however, very few genes correlate with isolation source; there is some evidence for increased horizontal gene transfer in strains isolated from fish and invertebrates, and evidence for increased biofilm formation in strains isolated from food-processing environments. Ancestral character estimation supports that  Psychrobacter  are descended from a warm host-associated bacterium. Finally, there is no correlation between isolation source and cold-adaptation of proteins. Overall, my results show that isolation source has some impact on  Psychrobacter genome evolution, though the population structure of the genus makes it difficult to disentangle its effects.


```{r setup, echo=FALSE}
setwd("~/ownCloud/Psychrobacter/Data_Analysis/PANX/thesis/")
library(stringr)
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggpubr)
library(ggsignif)

library(seqinr)
library(reshape2)
library(phytools)

library(micropan)
library(corrplot)

library(treeWAS)

psych_PCAT = read.delim("Psych_20201016_PCAT.txt", stringsAsFactors = F)
uniref_vPsychrobacter_blast <- read.delim("Psych_20201016_vs_uniref_CUSTOMBLASToutput.txt", header = T, stringsAsFactors = F)

P.tree = read.tree("Psychfinal_outgroupMoraxlincolnii.nwk")
P.tree = root(P.tree, "Moraxella_lincolnii", resolve.root = T)
P.tree.noout = drop.tip(P.tree, "Moraxella_lincolnii")
P.tree.noout$edge.length[P.tree.noout$edge.length == 0] <- 0.000000001

psych_genome_summaries = read.delim("genome_summary_statistics.txt", stringsAsFactors = F)
psych_strain_collection = read.delim("Psychrobacter_straininfo_mastertable.txt", stringsAsFactors = F)

psych_eggnog_annotations = read.delim("Psych_20201016_eggnog_clusters_annotations.txt", stringsAsFactors = F)

psych_gpa = read.delim("psych_20201016_nodash_gpa.txt", stringsAsFactors = F)

ISMEcolors = c("#F9D503", "#5DC863", "#21908C", "#3B528B", "#440154")


#output 
uniref_PCAT = read.delim("psych20201016_unirefPCAT.txt", stringsAsFactors = F)
psych_highPCAT = read.delim("psych_highPCAT.txt", stringsAsFactors = F) #strain names in the RDS file have the funky dashes issue
psych_medloPCAT = readRDS("Psychrobacter_nothighPCAT_geneproducts.RDS")
genes_mat_forpan = read.delim("genematrix_numGCperisolate.txt", stringsAsFactors = F, row.names = 1)
multicog_geneclusters = read.delim("genecluster_COG_categories.txt", stringsAsFactors = F)

strain_collection = read.delim("PsychrobacterPLUS_straininfo_mastertable.txt", stringsAsFactors = F)

M.tree = read.tree("PsychrobacterMoraxellaphylogeny_Apuyangensisoutgroup.nwk")
M.tree = root(M.tree, "A_puyangensis", resolve.root = T)
M.tree$edge.length[M.tree$edge.length == 0] <- 0.000000001

ISMEcolors = c("#F9D503", "#5DC863", "#21908C", "#3B528B", "#440154")

genematrix_treewas = read.delim("genematrix_for_treewas_fixeddashes.txt", stringsAsFactors = F, row.names = 1)

```

```{r}
sessionInfo()
```

 
 
#Pan-genome summary
```{r}
genematrix = psych_gpa[,10:ncol(psych_gpa)] #or whichever column your isolate data starts at
rownames(genematrix) = psych_gpa$geneCluster
genematrix = apply(genematrix,  2,  function(x) gsub("^$|^ $",  NA,  x)) #replace empty cells with NA

transform_func = function(x) { if (is.na(x) > 0) { r = 0 } else { r = 1 } } #creates a function to recognize when a cell has information in it (in this case,  when an isolate HAS a gene) and replace that cell's contents with "1". otherwise,  replace that cell's contents with "0"
genematrix_binary <- apply(genematrix, 2, function(x){sapply(x,  transform_func)}) #loop the function created in the step above through our transposed gene matrix

genematrix_binary_t = t(genematrix_binary) #transpose the matrix; need strains to be on the rows and genes to be on the columns
genematrix_binary_t = as.data.frame(genematrix_binary_t)
genematrix_binary_t = genematrix_binary_t %>%
  mutate_if(is.factor, as.numeric) #t() will transform your data into the "matrix" format, and treewas wants it in a dataframe format, so this transforms it back
rownames(genematrix_binary_t) = colnames(genematrix_binary)
#to produce output file "genematrix_for_treewas_fixeddashes.txt"


genes_mat = genematrix 
genes_mat_numbered = genes_mat
for(i in 1:nrow(genes_mat)) {
  for (j in 1:ncol(genes_mat)) {
    cell = genes_mat[i,j]
    num = str_count(cell, ",") + 1
    if (is.na(cell) == TRUE) {num = 0}
    genes_mat_numbered[i,j] = num
  }
}
rownames(genes_mat_numbered) = psych_gpa$geneCluster
genes_mat_numbered = as.data.frame(genes_mat_numbered)
genes_mat_numbered = genes_mat_numbered %>%
  mutate_all(as.character) %>%
  mutate_all(as.numeric)

library(micropan)
genes_mat_forpan = t(genes_mat_numbered)
colnames(genes_mat_forpan) = rownames(genes_mat)
rare = rarefaction(pan.matrix = genes_mat_forpan, n.perm = 999)
rare.melt = reshape2::melt(rare, id = c("Genome"))

(panrarefaction = ggplot(data = rare.melt, aes(x = Genome, y = value)) + 
    geom_boxplot(aes(x = Genome, group = Genome), outlier.shape = NA) + 
    theme_classic() + 
    xlab("Number of Genomes") + ylab("Number of Novel Orthologs"))

heaps(pan.matrix = genes_mat_forpan, n.perm = 999)

warm = psych_strain_collection %>% filter(ISME_source == "warm host") %>% select(strainID) %>% unlist() %>% unname()
other = psych_strain_collection %>% filter(ISME_source == "other host") %>% select(strainID) %>% unlist() %>% unname()
food = psych_strain_collection %>% filter(ISME_source == "food") %>% select(strainID) %>% unlist() %>% unname()
marine = psych_strain_collection %>% filter(ISME_source == "marine") %>% select(strainID) %>% unlist() %>% unname()
terrestrial = psych_strain_collection %>% filter(ISME_source == "terrestrial") %>% select(strainID) %>% unlist() %>% unname()

allseqsum = rowSums(genes_mat_numbered)
names(allseqsum) = rownames(genes_mat)
allisosum = rowSums(genematrix_binary)
names(allisosum) = rownames(genes_mat)

pangenome_by_isolation_summary = rbind(allseqsum, allisosum)

pangenome_by_isolation_summary = t(pangenome_by_isolation_summary)
pangenome_by_isolation_summary = as.data.frame(pangenome_by_isolation_summary)
pangenome_by_isolation_summary$geneCluster = rownames(pangenome_by_isolation_summary)


pangenome_by_isolation_summary$overall_pan_cat = NA
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum >= 84] <- "core"
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum <= 83 & pangenome_by_isolation_summary$allisosum >= 77] <- "soft core"
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum <= 76 & pangenome_by_isolation_summary$allisosum >= 2 ] <- "shell"
pangenome_by_isolation_summary$overall_pan_cat[pangenome_by_isolation_summary$allisosum == 1 ] <- "cloud"

pangenome_by_isolation_summary$overall_pan_cat = factor(pangenome_by_isolation_summary$overall_pan_cat, 
                                                        levels = c("core", "soft core", "shell", "cloud"))

```

###Figure 3.1 pan-genome rarefaction and categories
```{r}
(pancategory_count = ggplot(data = pangenome_by_isolation_summary, aes(x = overall_pan_cat)) + 
    geom_bar(stat = 'count') +
    theme_classic()
)

ggarrange(pancategory_count, panrarefaction, align = 'h', widths = c(1, 2))


```
###Figure 3.2 functional analysis of different pan-genome categories 

```{r}
COG_multi_cats = multicog_geneclusters %>% filter(COG2 != "")
#just realized that this does not have every COG cat for every strain, for example there is no COG CAT "B" for frigidicola_056...
#will have to make a new data frame to populate from scratch
COG_multi_cats_separated = data.frame()
for(i in 1:nrow(COG_multi_cats)) {
  gen = COG_multi_cats[i,1]
  cat = COG_multi_cats[i,2]
  COG1 = COG_multi_cats[i,3]
  dummy1 = c(gen, cat, COG1)
  COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy1), stringsAsFactors = F)
  COG2 = COG_multi_cats[i,4]
  dummy2 = c(gen, cat, COG2)
  COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy2), stringsAsFactors = F)
  COG3 = COG_multi_cats[i,5]
  if (COG3 != ""){
    dummy3 = c(gen, cat, COG3)
    COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy3), stringsAsFactors = F)
  }
  COG4 = COG_multi_cats[i,6]
  if (COG4 != ""){
    dummy4 = c(gen, cat, COG4)
    COG_multi_cats_separated = rbind.data.frame(COG_multi_cats_separated, as.character(dummy4), stringsAsFactors = F)
  }
}
colnames(COG_multi_cats_separated) = c("geneCluster", "pan_category", "COG_category")

COG_single_cats = multicog_geneclusters %>% filter(COG2 == "") %>%
  select(geneCluster, pan_category, COG1)
colnames(COG_single_cats)[3] = "COG_category"

multiCOG_genes_corrected = rbind(COG_multi_cats_separated, COG_single_cats)

multiCOG_genes_corrected$pan_category = factor(multiCOG_genes_corrected$pan_category, 
                                               levels = c("core", "softcore", "shell", "cloud"))

multiCOG_genes_corrected$COG_category = 
  factor(multiCOG_genes_corrected$COG_category, levels = c("D", "M", "N", "O", "T", "U", "V", "W", "Z",
                                                "A", "B","J", "K", "L","C", "E", "F", "G", "H", "I", "P", "Q",
                                                "S", "X"))


COGcols = c("#72300C", "#93074D", "#D60D69", "#ED5FA6", "#CE1B13", "#A50505", "#E5530A", "#E2770C", "#F9A138", 
         "#F7F072", "#F7D239", "#CCF43B", "#5BB73D", "#057F28", 
         "#05541A", "#20998B", "#60B1F4", "#1458F2", "#5141AF", "#1F048E", "#310872", "#520775", 
         "#9F9FA0", "#39383A")

(function_pancat = ggplot(data = multiCOG_genes_corrected, 
                          aes(x = pan_category, fill = COG_category)) + 
    geom_bar(stat = 'count', position = 'fill') + 
    scale_fill_manual(values = COGcols) +
    theme_classic())


COG_pancat_chi = chisq.test(t(table(multiCOG_genes_corrected$pan_category, multiCOG_genes_corrected$COG_category)))
COG_pancat_chi
COG_pancat_cor = corrplot(COG_pancat_chi$residuals, is.cor = FALSE)

#add function_pancat and COG_pancat_cor together in Illustrator

```


###Figure 3.3 and stats comparisons - proportion of genome devoted to each pan genome category
```{r}
psych_PCAT = left_join(psych_PCAT, pangenome_by_isolation_summary[,3:4])

test = left_join(psych_PCAT[,c(2:4,33)], psych_strain_collection[,c(3,27)])

test2 = as.data.frame(table(test$strainID, test$overall_pan_cat))
colnames(test2) = c("strainID", "pan_cat", "frequency")
test2 = left_join(test2, psych_strain_collection[,c(3,27)])

number_genes_perstrain = as.data.frame(table(psych_PCAT$strainID))
colnames(number_genes_perstrain) = c("strainID", "num_genes")
test2 = left_join(test2, number_genes_perstrain)
test2$ISME_source[test2$strainID == "P_sp_IAM12030_72-O-c"] <- "terrestrial"


agg_pancat_byiso_bygenome = aggregate(test2$frequency, by = list(test2$ISME_source, test2$pan_cat), FUN = mean)
colnames(agg_pancat_byiso_bygenome) = c("isolation_source", "pan_cat", "average_geneclusters_pergenome")
agg_pancat_byiso_bygenome$pan_cat = factor(agg_pancat_byiso_bygenome$pan_cat, levels = c("core", "soft core", "shell", "cloud"))
agg_pancat_byiso_bygenome_sp = spread(agg_pancat_byiso_bygenome, key = pan_cat, value = average_geneclusters_pergenome)

chisq_pan_iso = chisq.test(agg_pancat_byiso_bygenome_sp[1:5,2:5])
corrplot(chisq_pan_iso$residuals, is.corr = F)

aggregate(test2$frequency, by = list(test2$pan_cat), FUN = mean)

pancat_byiso_bygenome = as.data.frame(table(psych_PCAT$strainID, psych_PCAT$overall_pan_cat))
colnames(pancat_byiso_bygenome) = c("strainID", "pan_cat", "frequency")
pancat_byiso_bygenome = left_join(pancat_byiso_bygenome, psych_strain_collection[,c(3,27)])
pancat_byiso_bygenome = left_join(pancat_byiso_bygenome, number_genes_perstrain)

  
pancat_byiso_bygenome$ISME_source = factor(pancat_byiso_bygenome$ISME_source, 
                                           levels = c("warm host", "other host", "food", "marine", "terrestrial"))
pancat_byiso_bygenome$pan_cat = factor(pancat_byiso_bygenome$pan_cat, 
                                           levels = c("core", "soft core", "shell", "cloud"))

pancat_byiso_bygenome$ISME_source[pancat_byiso_bygenome$strainID == "P_sp_IAM12030_72-O-c"] <- "terrestrial"


pancat_byiso_bygenome_sp = spread(pancat_byiso_bygenome, key = "pan_cat", value = "frequency")
pancat_byiso_bygenome_sp$core_prop = pancat_byiso_bygenome_sp$core/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_sp$softcore_prop = pancat_byiso_bygenome_sp$`soft core`/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_sp$shell_prop = pancat_byiso_bygenome_sp$shell/pancat_byiso_bygenome_sp$num_genes
pancat_byiso_bygenome_sp$cloud_prop = pancat_byiso_bygenome_sp$cloud/pancat_byiso_bygenome_sp$num_genes

pancat_byiso_bygenome_ga = pancat_byiso_bygenome_sp %>% 
  gather(core_prop, softcore_prop, shell_prop, cloud_prop, key = "pan_cat", value = "proportion") %>%
  select(-core, -`soft core`, -shell, -cloud)

pancat_byiso_bygenome_ga$pan_cat = str_remove(pancat_byiso_bygenome_ga$pan_cat, "_prop")
pancat_byiso_bygenome_ga$pan_cat = factor(pancat_byiso_bygenome_ga$pan_cat, 
                                          levels = c("core", "softcore", "shell", "cloud"))


(genenum_iso = ggplot(data = pancat_byiso_bygenome_ga, aes(x = ISME_source, y= num_genes, color = ISME_source)) + 
  geom_boxplot(alpha = 0) + 
  geom_point(position=position_dodge(width=0.75),aes(group=ISME_source)) +
  scale_color_manual(values = ISMEcolors) +
  #facet_grid(.~pan_cat, scales = "free") +
  theme_classic())

(pan_str_iso = ggplot(data = pancat_byiso_bygenome_ga, aes(x = ISME_source, y= proportion, color = ISME_source)) + 
  geom_boxplot(alpha = 0) + 
  geom_point(position=position_dodge(width=0.75),aes(group=ISME_source)) +
  scale_color_manual(values = ISMEcolors) +
  facet_grid(.~pan_cat, scales = "free") +
  theme_classic())

stats_core = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'core')
kruskal.test(proportion ~ ISME_source, data = stats_core)
pairwise.wilcox.test(x = stats_core$proportion, g = stats_core$ISME_source, p.adjust.method = 'BH')
aggregate(stats_core$proportion, by = list(stats_core$ISME_source), FUN = median)

stats_softcore = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'softcore')
kruskal.test(proportion ~ ISME_source, data = stats_softcore)
pairwise.wilcox.test(x = stats_softcore$proportion, g = stats_softcore$ISME_source, p.adjust.method = 'BH')
aggregate(stats_softcore$proportion, by = list(stats_softcore$ISME_source), FUN = median)

stats_shell = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'shell')
kruskal.test(proportion ~ ISME_source, data = stats_shell)
pairwise.wilcox.test(x = stats_shell$proportion, g = stats_shell$ISME_source, p.adjust.method = 'BH')
aggregate(stats_shell$proportion, by = list(stats_shell$ISME_source), FUN = median)

stats_cloud = pancat_byiso_bygenome_ga %>% filter(pan_cat == 'cloud')
kruskal.test(proportion ~ ISME_source, data = stats_cloud)
pairwise.wilcox.test(x = stats_cloud$proportion, g = stats_cloud$ISME_source, p.adjust.method = 'BH')
aggregate(stats_cloud$proportion, by = list(stats_cloud$ISME_source), FUN = median)

```

###Figure 3.4 unique gene content between isolation sources
```{r}
warm = psych_strain_collection %>% filter(ISME_source == "warm host") %>% select(strainID) %>% unlist() %>% unname()
other = psych_strain_collection %>% filter(ISME_source == "other host") %>% select(strainID) %>% unlist() %>% unname()
food = psych_strain_collection %>% filter(ISME_source == "food") %>% select(strainID) %>% unlist() %>% unname()
marine = psych_strain_collection %>% filter(ISME_source == "marine") %>% select(strainID) %>% unlist() %>% unname()
terrestrial = psych_strain_collection %>% filter(ISME_source == "terrestrial") %>% select(strainID) %>% unlist() %>% unname()

warmseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% warm])
names(warmseqsum) = rownames(genes_mat)
warmisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% warm])
names(warmisosum) = rownames(genes_mat)

otherseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% other])
names(otherseqsum) = rownames(genes_mat)
otherisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% other])
names(otherisosum) = rownames(genes_mat)

foodseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% food])
names(otherseqsum) = rownames(genes_mat)
foodisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% food])
names(foodisosum) = rownames(genes_mat)

marineseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% marine])
names(marineseqsum) = rownames(genes_mat)
marineisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% marine])
names(marineisosum) = rownames(genes_mat)

terrestrialseqsum = rowSums(genes_mat_numbered[,colnames(genes_mat_numbered) %in% terrestrial])
names(terrestrialseqsum) = rownames(genes_mat)
terrestrialisosum = rowSums(genematrix_binary[,colnames(genematrix_binary) %in% terrestrial])
names(terrestrialisosum) = rownames(genes_mat)

allseqsum = rowSums(genes_mat_numbered)
names(allseqsum) = rownames(genes_mat)
allisosum = rowSums(genematrix_binary)
names(allisosum) = rownames(genes_mat)

pan_str_iso = rbind(allseqsum, allisosum, warmseqsum, warmisosum, otherseqsum, otherisosum, 
                                       foodseqsum, foodisosum, marineseqsum, marineisosum, terrestrialseqsum, terrestrialisosum)

pan_str_iso = t(pan_str_iso)
pan_str_iso = as.data.frame(pan_str_iso)
pan_str_iso$geneCluster = rownames(pan_str_iso)


pan_str_iso$overall_pan_cat = NA
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum >= 84] <- "core"
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum <= 83 & pan_str_iso$allisosum >= 77] <- "soft core"
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum <= 82 & pan_str_iso$allisosum >= 2 ] <- "shell"
pan_str_iso$overall_pan_cat[pan_str_iso$allisosum == 1 ] <- "cloud"

pan_str_iso$warm_pan_cat = NA
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum >= 13] <- "core"
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum <= 12 & pan_str_iso$warmisosum >= 2 ] <- "shell"
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum == 1 ] <- "cloud"
pan_str_iso$warm_pan_cat[pan_str_iso$warmisosum == 0 ] <- "absent"

pan_str_iso$other_pan_cat = NA
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum >= 20] <- "core"
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum <= 19 & pan_str_iso$otherisosum >= 2 ] <- "shell"
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum == 1 ] <- "cloud"
pan_str_iso$other_pan_cat[pan_str_iso$otherisosum == 0 ] <- "absent"

pan_str_iso$food_pan_cat = NA
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum == 11] <- "core"
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum <= 10 & pan_str_iso$foodisosum >= 2 ] <- "shell"
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum == 1 ] <- "cloud"
pan_str_iso$food_pan_cat[pan_str_iso$foodisosum == 0 ] <- "absent"

pan_str_iso$marine_pan_cat = NA
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum >= 19] <- "core"
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum <= 18 & pan_str_iso$marineisosum >= 2 ] <- "shell"
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum == 1 ] <- "cloud"
pan_str_iso$marine_pan_cat[pan_str_iso$marineisosum == 0 ] <- "absent"

pan_str_iso$terrestrial_pan_cat = NA
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum >= 15] <- "core"
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum <= 14 & pan_str_iso$terrestrialisosum >= 2 ] <- "shell"
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum == 1 ] <- "cloud"
pan_str_iso$terrestrial_pan_cat[pan_str_iso$terrestrialisosum == 0 ] <- "absent"

unique_warm = pan_str_iso %>% filter(warm_pan_cat != "absent" & other_pan_cat == "absent" & food_pan_cat == "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_warm_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_warm)
unique_warm_annot$isolation = "warm"

unique_other = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat != "absent" & food_pan_cat == "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_other_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_other)
unique_other_annot$isolation = "other"

unique_food = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat == "absent" & food_pan_cat != "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_food_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_food)
unique_food_annot$isolation = "food"

unique_marine = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat == "absent" & food_pan_cat == "absent" & marine_pan_cat != "absent" & terrestrial_pan_cat == "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_marine_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_marine)
unique_marine_annot$isolation = "marine"

unique_terrestrial = pan_str_iso %>% filter(warm_pan_cat == "absent" & other_pan_cat == "absent" & food_pan_cat == "absent" & marine_pan_cat == "absent" & terrestrial_pan_cat != "absent") %>% select(geneCluster) %>% unlist() %>% unname
unique_terrestrial_annot = multiCOG_genes_corrected %>% filter(geneCluster %in% unique_terrestrial)
unique_terrestrial_annot$isolation = "terrestrial"

unique_genes_per_isolation = rbind(unique_warm_annot, unique_other_annot, unique_food_annot, unique_marine_annot, unique_terrestrial_annot)

unique_genes_per_isolation$isolation = factor(unique_genes_per_isolation$isolation, levels = c("warm", "other", "food", "marine", "terrestrial"))
unique_genes_per_isolation$COG_category = factor(unique_genes_per_isolation$COG_category, levels = c("D", "M", "N", "O", "T", "U", "V", "W", "Z",
                                                                                 "A", "B","J", "K", "L",
                                                                                 "C", "E", "F", "G", "H", "I", "P", "Q",
                                                                                 "S", "X"))
cols = c("#72300C", "#93074D", "#D60D69", "#ED5FA6", "#CE1B13", "#A50505",
         "#E5530A", "#E2770C", "#F9A138", "#F7F072", "#F7D239", "#CCF43B",
         "#5BB73D", "#057F28", "#05541A", "#20998B", "#60B1F4", "#1458F2",
         "#5141AF", "#1F048E", "#310872", "#520775", "#9F9FA0", "#39383A")

(unique_gene_content = ggplot(data = unique_genes_per_isolation, aes(x = isolation, fill = COG_category)) +
  geom_bar(stat = 'count') +
  scale_fill_manual(values = cols) +
  theme_classic())

unique_corr = table(unique_genes_per_isolation$isolation, unique_genes_per_isolation$COG_category)
chisq_uni_iso = chisq.test(unique_corr)
corrplot(chisq_uni_iso$residuals, is.corr = F)

#add unique_gene_content w corrplot in illustrator

```

#Pan-genome wide association study with isolation source as trait. 
```{r}
unique(psych_strain_collection$ISME_source)

isolationsource = psych_strain_collection[,c(3,27)]
isolationsource$dummy = 1
isolationsource = isolationsource %>%
  spread(key = ISME_source, value = dummy)
isolationsource[is.na(isolationsource)] <- 0
rownames(isolationsource) = isolationsource$strainID

WH <- as.vector(unlist(isolationsource$`warm host`))
names(WH) = rownames(isolationsource)
all(names(WH) %in% rownames(genematrix_treewas))
all(rownames(genematrix_treewas) %in% names(WH))
WH_treewas <- treeWAS(snps = genematrix_treewas, phen = WH, tree = P.tree.noout, seed = 1)
print(WH_treewas)

OH <- as.vector(unlist(isolationsource$`other host`))
names(OH) = rownames(isolationsource)
all(names(OH) %in% rownames(genematrix_treewas))
all(rownames(genematrix_treewas) %in% names(OH))
OH_treewas <- treeWAS(snps = genematrix_treewas, phen = OH, tree = P.tree.noout, seed = 1)
print(OH_treewas)

Fo <- as.vector(unlist(isolationsource$food))
names(Fo) = rownames(isolationsource)
all(names(Fo) %in% rownames(genematrix_treewas))
all(rownames(genematrix_treewas) %in% names(Fo))
Fo_treewas <- treeWAS(snps = genematrix_treewas, phen = Fo, tree = P.tree.noout, seed = 1)
print(Fo_treewas)

Ma <- as.vector(unlist(isolationsource$marine))
names(Ma) = rownames(isolationsource)
all(names(Ma) %in% rownames(genematrix_treewas))
all(rownames(genematrix_treewas) %in% names(Ma))
Ma_treewas <- treeWAS(snps = genematrix_treewas, phen = Ma, tree = P.tree.noout, seed = 1)
print(Ma_treewas)

Te <- as.vector(unlist(isolationsource$terrestrial))
names(Te) = rownames(isolationsource)
all(names(Te) %in% rownames(genematrix_treewas))
all(rownames(genematrix_treewas) %in% names(Te))
Te_treewas <- treeWAS(snps = genematrix_treewas, phen = Te, tree = P.tree.noout, seed = 1)
print(Te_treewas)

OH_treewas_gc = OH_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
OH_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% OH_treewas_gc)

Fo_treewas_gc = Fo_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
Fo_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% Fo_treewas_gc)

Ma_treewas_gc = Ma_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
Ma_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% Ma_treewas_gc)

Te_treewas_gc = Te_treewas$treeWAS.combined$treeWAS.combined %>% unlist()
Te_treewas_annotated = psych_eggnog_annotations %>% filter(geneCluster %in% Te_treewas_gc)

```

#Isolation source ancestral character estimation.

```{r}

#two state ancestral state reconstruction
ordered_df = strain_collection[match(M.tree$tip.label, strain_collection$strainID),]
rownames(ordered_df) = ordered_df$strainID
x1<-setNames(ordered_df[,21],rownames(ordered_df))
x1col = c("deepskyblue3", "orange")

fitER1 = ace(x1, M.tree, model = "ER", type = "discrete", marginal = TRUE)
fitSYM1 = ace(x1, M.tree, model = "SYM", type = "discrete", marginal = TRUE)
fitARD1 = ace(x1, M.tree, model = "ARD", type = "discrete", marginal = TRUE)

#ARD model maximizes likelihood, but does it do so significantly?
1 - pchisq(2*abs(fitARD1$loglik - fitER1$loglik), 1) #p = 0.15
1 - pchisq(2*abs(fitARD1$loglik - fitSYM1$loglik), 1) #p = 0.15

#ok, so is there a difference between ER and SYM?
1 - pchisq(2*abs(fitER1$loglik - fitSYM1$loglik), 1)
#no
#so I will go with the simplest ER model

plotTree(M.tree,fsize=0.8,ftype="i")
nodelabels(node=1:M.tree$Nnode+Ntip(M.tree),pie=fitER1$lik.anc,piecol = x1col,cex=0.5)
#tiplabels(pie=to.matrix(x1[M.tree$tip.label],levels(x1)),piecol = x1col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x1)),colors=x1col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(M.tree)),fsize=0.8)

#plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x1,sort(unique(x1))),piecol=x1col,cex=0.3)
add.simmap.legend(colors=x1col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(M.tree)),fsize=0.8)

fitER1

psychroot = drop.tip(M.tree, c("M_boevrei","M_atlantae","M_osloensis","A_puyangensis",
                     "M_cuniculi","M_porci","M_pluranimalium","M_canis","M_catarrhalis","M_caviae",
                     "M_ovis","M_bovoculi","M_oblonga","M_equi","M_bovis","M_caprae","M_lacunata","M_nonliquefaciens"))


x1psychroot<-x1[names(x1) %in% psychroot$tip.label]
fitER_psychroot = ace(x1psychroot, psychroot, model = "ER", type = "discrete", marginal = TRUE)

fitER_psychroot

plotTree(psychroot,fsize=0.8,ftype="i")
nodelabels(node=1:psychroot$Nnode+Ntip(psychroot),pie=fitER_psychroot$lik.anc,piecol = x1col,cex=0.5)
#tiplabels(pie=to.matrix(x1psychroot[psychroot$tip.label],levels(x1psychroot)),piecol = x1col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x1psychroot)),colors=x1col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(psychroot)),fsize=0.8)

#plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x1psychroot,sort(unique(x1psychroot))),piecol=x1col,cex=0.3)
add.simmap.legend(colors=x1col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(psychroot)),fsize=0.8)


#now by more specific isolation sources 
ordered_df$ISME_source = factor(ordered_df$ISME_source,
                                levels = c("warm host", "other host", "food", "marine", "terrestrial"))
x2<-setNames(ordered_df[,19],rownames(ordered_df))
x2col = c("#F9D503", "#5DC863", "#21908C", "#3B528B", "#440154")

fitER2 = ace(x2, M.tree, model = "ER", type = "discrete", marginal = TRUE)
fitSYM2 = ace(x2, M.tree, model = "SYM", type = "discrete", marginal = TRUE)
fitARD2 = ace(x2, M.tree, model = "ARD", type = "discrete", marginal = TRUE)

#ARD model maximizes likelihood, but does it do so significantly?
1 - pchisq(2*abs(fitARD2$loglik - fitER2$loglik), 1) #p = 1.110223e-15
1 - pchisq(2*abs(fitARD2$loglik - fitSYM2$loglik), 1) #p = 4.260376e-05

#both significant, so
#I will go with the ARD model

1 - pchisq(2*abs(fitSYM2$loglik - fitER2$loglik), 1)

plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x2,sort(unique(x2))),piecol=x2col,cex=0.3)
add.simmap.legend(colors=x2col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(M.tree)),fsize=0.8)

#plotTree(M.tree,fsize=0.8,ftype="i")
nodelabels(node=1:M.tree$Nnode+Ntip(M.tree),pie=fitER2$lik.anc,piecol = x2col,cex=0.5)
#tiplabels(pie=to.matrix(x2[M.tree$tip.label],levels(x2)),piecol = x2col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x2)),colors=x2col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(M.tree)),fsize=0.8)

x2psychroot<-x2[names(x2) %in% psychroot$tip.label]
fitER2_psychroot = ace(x2psychroot, psychroot, model = "ER", type = "discrete", marginal = TRUE)

fitER2_psychroot

plotTree(psychroot,fsize=0.8,ftype="i")
nodelabels(node=1:psychroot$Nnode+Ntip(psychroot),pie=fitER2_psychroot$lik.anc,piecol = x2col,cex=0.5)
#tiplabels(pie=to.matrix(x2psychroot[psychroot$tip.label],levels(x1psychroot)),piecol = x2col,cex=0.3)
#add.simmap.legend(leg=sort(unique(x2psychroot)),colors=x2col,x=0.9*par()$usr[1],
#                  y=-max(nodeHeights(psychroot)),fsize=0.8)

#plotTree(M.tree,fsize=0.8,ftype="i")
tiplabels(pie=to.matrix(x2psychroot,sort(unique(x2psychroot))),piecol=x2col,cex=0.3)
add.simmap.legend(colors=x2col,prompt=FALSE,x=0.9*par()$usr[1],
                  y=-max(nodeHeights(psychroot)),fsize=0.8)

```


#Predicted cold-adapted protein traits.
###Figure 3.6
```{r}
psych_genecluster_summ = as.data.frame(table(psych_PCAT$geneCluster))
colnames(psych_genecluster_summ) = c("geneCluster", "frequency")

highpcatfreq = as.data.frame(table(psych_highPCAT$geneCluster))
colnames(highpcatfreq) = c("geneCluster", "freq_highPCAT")

psych_genecluster_summ = left_join(psych_genecluster_summ, highpcatfreq)
psych_genecluster_summ$freq_highPCAT[is.na(psych_genecluster_summ$freq_highPCAT) == TRUE] <- 0
psych_genecluster_summ$perc_highPCAT = psych_genecluster_summ$freq_highPCAT/psych_genecluster_summ$frequency

psych_genecluster_summ_nosingletons = psych_genecluster_summ %>% filter(frequency != 1)

(perHPCAT = ggplot(data = psych_genecluster_summ, aes(x = perc_highPCAT)) + geom_histogram() + theme_classic() + xlab("Percentage of CDS in geneCluster that are classified as 'highly cold adaptive'"))
(perHPCAT_noS = ggplot(data = psych_genecluster_summ_nosingletons, aes(x = perc_highPCAT)) + geom_histogram() + theme_classic() + xlab("Percentage of CDS in geneCluster that are classified as 'highly cold adaptive', singletons removed"))

ggarrange(perHPCAT, perHPCAT_noS, align = 'h', labels = c("A", "B"))
```

###Figure 3.7
```{r}
psych_PCAT_strainID_summ = as.data.frame(table(psych_PCAT$strainID))
colnames(psych_PCAT_strainID_summ) = c("strainID", "frequency")
highpcat_bystrain = as.data.frame(table(psych_highPCAT$strainID))
colnames(highpcat_bystrain) = c("strainID", "freq_highPCAT")

psych_PCAT_strainID_summ = left_join(psych_PCAT_strainID_summ, highpcat_bystrain)
psych_PCAT_strainID_summ$perc_highPCAT = psych_PCAT_strainID_summ$freq_highPCAT/psych_PCAT_strainID_summ$frequency

perc_highPCAT = psych_PCAT_strainID_summ$perc_highPCAT
names(perc_highPCAT) = psych_PCAT_strainID_summ$strainID
setdiff(names(perc_highPCAT), P.tree.noout$tip.label)
phylo_PCAT = phylosig(tree = P.tree.noout, x = perc_highPCAT, test = TRUE)
phylo_PCAT

(HPCAT_bystrain = ggplot(data = psych_PCAT_strainID_summ, aes(x = perc_highPCAT)) + geom_histogram() + theme_classic() + xlab("Percentage of highly cold adaptive proteins by strain"))

psych_genome_summaries = left_join(psych_genome_summaries, psych_strain_collection[,c(3,5,21,27)])
psych_PCAT_strainID_summ$strainID = as.character(psych_PCAT_strainID_summ$strainID)
psych_PCAT_strainID_summ$strainID[psych_PCAT_strainID_summ$strainID == "P_sp_IAM12030_72-O-c"] <- "P_sp_IAM12030-72-O-c"

psych_PCAT_strainID_summ = left_join(psych_PCAT_strainID_summ, psych_genome_summaries[,c(19,5,20,21)])
psych_PCAT_strainID_summ$temprange = "mesophile"
psych_PCAT_strainID_summ$temprange[psych_PCAT_strainID_summ$strainID == "P_frigidicola_ACAM304" |
                                     psych_PCAT_strainID_summ$strainID == "P_frigidicola_ACAM309" |
                                     psych_PCAT_strainID_summ$strainID == "P_sp_72-O-c" |
                                     psych_PCAT_strainID_summ$strainID == "P_sp_IAM12030-72-O-c" |
                                     psych_PCAT_strainID_summ$strainID == "P_urativorans_ACAM311"] <- "psychrophile"

wilcox.test(perc_highPCAT ~ temprange, data = psych_PCAT_strainID_summ)
kruskal.test(frequency ~ ISME_source, data = psych_PCAT_strainID_summ)
kruskal.test(Genome.size..bp. ~ ISME_source, data = psych_PCAT_strainID_summ)


(genes_by_isosource = ggplot(data = psych_PCAT_strainID_summ, aes(x = ISME_source, y = frequency)) + 
  geom_boxplot(alpha = 0, aes(color = ISME_source)) + 
    geom_point(aes(color = ISME_source)) +
    scale_color_manual(values = ISMEcolors) +
    geom_signif(comparisons = list(c("warm host", "other host"), c("warm host", "food"), c("other host", "marine"), c("other host", "terrestrial"), c("food", "marine"), c("food", "terrestrial")), map_signif_level = T) +
  theme_classic() +
    ylab("# of genes per genome"))

#all pairwise comparisons:
#comparisons = split(t(combn(levels(psych_PCAT_strainID_summ$ISME_source), 2)), seq(nrow(t(combn(levels(psych_PCAT_strainID_summ$ISME_source), 2)))))
pairwise.wilcox.test(x = psych_PCAT_strainID_summ$Genome.size..bp., g = psych_PCAT_strainID_summ$ISME_source, p.adjust.method = 'BH')
pairwise.wilcox.test(x = psych_PCAT_strainID_summ$frequency, g = psych_PCAT_strainID_summ$ISME_source, p.adjust.method = 'BH')

(HCA_by_temprange = ggplot(data = psych_PCAT_strainID_summ, aes(x = temprange, y = perc_highPCAT)) + 
    geom_boxplot(alpha = 0) +
    theme_classic() +
    ylab("% of genes that are highly cold adapted"))

psych_PCAT_strainID_summ$ISME_source = factor(psych_PCAT_strainID_summ$ISME_source, levels = c("warm host", "other host", "food", "marine", "terrestrial"))
(HCA_by_isosource = ggplot(data = psych_PCAT_strainID_summ, aes(x = ISME_source, y = perc_highPCAT)) + 
  geom_boxplot(alpha = 0, aes(color = ISME_source)) + 
    geom_point(aes(color = ISME_source)) +
    scale_color_manual(values = ISMEcolors) +
  theme_classic() +
    ylab("% of genes that are highly cold adapted"))

kruskal.test(perc_highPCAT ~ ISME_source, data = psych_PCAT_strainID_summ)

ggarrange(HCA_by_temprange, HCA_by_isosource, labels = c("A", "B"), widths = c(1, 3))
```

###figure 3.8
```{r}
highPCATlocus = psych_highPCAT %>% select(locus_tag) %>% unlist() %>% unname()
psych_PCAT$PCAT = NA
psych_PCAT$PCAT[psych_PCAT$locus_tag %in% highPCATlocus] <- "high"
psych_PCAT$PCAT[is.na(psych_PCAT$PCAT) == TRUE] <- "not"
psych_PCAT$PCAT = factor(psych_PCAT$PCAT, levels = c("not", "high"))

psych_PCAT_annot = left_join(psych_PCAT, multiCOG_genes_corrected)

(function_PCAT = ggplot(data = psych_PCAT_annot, 
                          aes(x = PCAT, fill = COG_category)) + 
    geom_bar(stat = 'count', position = 'fill') + 
    scale_fill_manual(values = COGcols) +
    theme_classic())

COG_PCAT_chi = chisq.test(t(table(psych_PCAT_annot$PCAT, psych_PCAT_annot$COG_category)))
COG_PCAT_chi
COG_PCAT_cor = corrplot(COG_PCAT_chi$residuals, is.cor = FALSE)

#arrange function_PCAT w corrplot in illustrator
```



